Analysis date: 2023-02-10

Depends on

DIPG_FirstBatch_DataProcessing Script

load("../Data/Cache/Xenografts_Batch1_2_DataProcessing.RData")

TODO

  • Do differential abudance analysis for prep batch and mass spec run

Setup

Load libraries and functions

Analysis

DEP

Tyrosine all

Each condition vs ctrl

data_diff_ctrl_vs_E_pY <- test_diff(pY_se_Set2, type="manual", test = "E_vs_ctrl")
## Tested contrasts: E_vs_ctrl
dep_ctrl_vs_E_pY <- add_rejections_SH(data_diff_ctrl_vs_E_pY, alpha = 0.05, lfc = log2(1.2))
GGPlotly_Volcano(dep_ctrl_vs_E_pY, contrast = "E_vs_ctrl", 
                add_names = TRUE,
                additional_title = "pY")
## Warning: `gather_()` was deprecated in tidyr 1.2.0.
## ℹ Please use `gather()` instead.
## ℹ The deprecated feature was likely used in the plotly package.
##   Please report the issue at <]8;;https://github.com/plotly/plotly.R/issueshttps://github.com/plotly/plotly.R/issues]8;;>.
Return_DEP_Hits_Plots(data = pY_Set2_form, dep_ctrl_vs_E_pY, comparison = "E_vs_ctrl_diff")
## Warning: Using an external vector in selections was deprecated in tidyselect 1.1.0.
## ℹ Please use `all_of()` or `any_of()` instead.
##   # Was:
##   data %>% select(comparison)
## 
##   # Now:
##   data %>% select(all_of(comparison))
## 
## See <https://tidyselect.r-lib.org/reference/faq-external-vector.html>.
## 'select()' returned 1:many mapping between keys and columns
## Loading required namespace: reactome.db
## 'select()' returned 1:many mapping between keys and columns
## 'select()' returned 1:1 mapping between keys and columns

##                                                            pathway      pval
## 1: A tetrasaccharide linker sequence is required for GAG synthesis 0.8604207
## 2:                                       ABC transporter disorders 0.3480392
## 3:                          ABC-family proteins mediated transport 0.2157895
## 4:                       ADP signalling through P2Y purinoceptor 1 0.7151052
## 5:                                           ALK mutants bind TKIs 0.3688363
## 6:               APC/C-mediated degradation of cell cycle proteins 0.1157895
##         padj    log2err         ES        NES size              leadingEdge
## 1: 0.9665167 0.05049830  0.4661458  0.6940019    2                6385,1464
## 2: 0.8984150 0.11237852 -0.5432428 -1.1208733    5      5696,5687,5692,5694
## 3: 0.8226559 0.15315881 -0.5602097 -1.2386975    6 5696,5687,1965,5692,5694
## 4: 0.9665167 0.05909548  0.5600116  0.8337500    2                1432,6714
## 5: 0.9034388 0.09528798  0.8337662  1.1032501    1                     1213
## 6: 0.8043813 0.21392786 -0.6286041 -1.3899265    6            983,5696,5687
data_diff_EC_vs_ctrl_pY <- test_diff(pY_se_Set2, type="manual", test = "EC_vs_ctrl")
## Tested contrasts: EC_vs_ctrl
dep_EC_vs_ctrl_pY <- add_rejections_SH(data_diff_EC_vs_ctrl_pY, alpha = 0.05, lfc = log2(1.2))
GGPlotly_Volcano(dep_EC_vs_ctrl_pY, contrast = "EC_vs_ctrl", 
                 add_names = TRUE,
                additional_title = "pY") 
Return_DEP_Hits_Plots(data = pY_Set2_form, dep_EC_vs_ctrl_pY, comparison = "EC_vs_ctrl_diff")
## 'select()' returned 1:many mapping between keys and columns
## 'select()' returned 1:many mapping between keys and columns
## 'select()' returned 1:1 mapping between keys and columns

##                                                            pathway      pval
## 1: A tetrasaccharide linker sequence is required for GAG synthesis 0.3234624
## 2:                                       ABC transporter disorders 0.3828125
## 3:                          ABC-family proteins mediated transport 0.4647676
## 4:                       ADP signalling through P2Y purinoceptor 1 0.8883827
## 5:                                           ALK mutants bind TKIs 0.6749049
## 6:               APC/C-mediated degradation of cell cycle proteins 0.3133433
##         padj    log2err         ES        NES size              leadingEdge
## 1: 0.6800285 0.11237852  0.6796875  1.0967802    2                1464,6385
## 2: 0.6975069 0.08020234 -0.5289330 -1.0595494    5      5692,5696,5693,5687
## 3: 0.7576935 0.06815134 -0.4761114 -1.0062663    6 5692,5696,5693,5687,1965
## 4: 0.9367712 0.05712585  0.4401042  0.7101757    2                1432,6714
## 5: 0.8551070 0.06157068  0.6649351  0.8835653    1                     1213
## 6: 0.6800285 0.08889453 -0.5325901 -1.1256346    6  5692,983,5696,5693,5687
Plot_Enrichment_Single_Pathway(dep_EC_vs_ctrl_pY, comparison = "EC_vs_ctrl_diff", 
                               pw = "Epigenetic regulation of gene expression")
data_diff_EBC_vs_ctrl_pY <- test_diff(pY_se_Set2, type="manual", test = "EBC_vs_ctrl")
## Tested contrasts: EBC_vs_ctrl
dep_EBC_vs_ctrl_pY <- add_rejections_SH(data_diff_EBC_vs_ctrl_pY, alpha = 0.05, lfc = log2(1.2))
GGPlotly_Volcano(dep_EBC_vs_ctrl_pY, contrast = "EBC_vs_ctrl", 
                 add_names = TRUE,
                additional_title = "pY")
Return_DEP_Hits_Plots(data = pY_Set2_form, dep_EBC_vs_ctrl_pY, comparison = "EBC_vs_ctrl_diff")
## 'select()' returned 1:many mapping between keys and columns
## 'select()' returned 1:many mapping between keys and columns
## 'select()' returned 1:1 mapping between keys and columns

##                                                            pathway      pval
## 1: A tetrasaccharide linker sequence is required for GAG synthesis 0.2043011
## 2:                                       ABC transporter disorders 0.4234568
## 3:                          ABC-family proteins mediated transport 0.5950992
## 4:                       ADP signalling through P2Y purinoceptor 1 0.9207607
## 5:                                           ALK mutants bind TKIs 0.2625000
## 6:               APC/C-mediated degradation of cell cycle proteins 0.2357060
##         padj    log2err         ES        NES size        leadingEdge
## 1: 0.5304528 0.15964670  0.7317708  1.2440878    2               1464
## 2: 0.6651280 0.06321912 -0.5071326 -1.0345363    5     5693,5696,5692
## 3: 0.7900302 0.04477489 -0.4241333 -0.9098507    6     5693,5696,5692
## 4: 0.9733705 0.03879622 -0.4322917 -0.6811524    2          6714,1432
## 5: 0.5746246 0.12043337 -0.8779221 -1.1654585    1               1213
## 6: 0.5470826 0.09082414 -0.5568822 -1.1946236    6 983,5693,5696,5692

EC vs E

data_diff_EC_vs_E_pY <- test_diff(pY_se_Set2, type = "manual", 
                              test = c("EC_vs_E"))
## Tested contrasts: EC_vs_E
dep_EC_vs_E_pY <- add_rejections_SH(data_diff_EC_vs_E_pY, alpha = 0.05, lfc = log2(1.2))
GGPlotly_Volcano(dep_EC_vs_E_pY, additional_title = "pY", contrast = "EC_vs_E", proteins_of_interest = "EGFR")
Return_DEP_Hits_Plots(data = pY_Set2_form, dep_EC_vs_E_pY, comparison = "EC_vs_E_diff")
## 'select()' returned 1:many mapping between keys and columns
## 'select()' returned 1:many mapping between keys and columns
## 'select()' returned 1:1 mapping between keys and columns

##                                                            pathway      pval
## 1: A tetrasaccharide linker sequence is required for GAG synthesis 0.4781421
## 2:                                       ABC transporter disorders 0.4330025
## 3:                          ABC-family proteins mediated transport 0.5592814
## 4:                       ADP signalling through P2Y purinoceptor 1 0.7755102
## 5:                                           ALK mutants bind TKIs 0.2821497
## 6:               APC/C-mediated degradation of cell cycle proteins 0.7293413
##         padj    log2err         ES        NES size leadingEdge
## 1: 0.7855344 0.09923333  0.5703125  0.9629962    2   1464,6385
## 2: 0.7499462 0.06238615 -0.5436825 -1.0406948    5   5692,5693
## 3: 0.8169538 0.04879897 -0.4719043 -0.9484551    6   5692,5693
## 4: 0.9221278 0.04623025 -0.5208333 -0.7943202    2   6714,1432
## 5: 0.6722759 0.11012226 -0.8545455 -1.1385447    1        1213
## 6: 0.9054648 0.03660822 -0.4094728 -0.8229774    6   5692,5693

## Note: Row-scaling applied for this heatmap

#data_results <- get_df_long(dep)

EBC vs EC

data_diff_EBC_vs_EC_pY <- test_diff(pY_se_Set2, type = "manual", 
                              test = c("EBC_vs_EC"))
## Tested contrasts: EBC_vs_EC
dep_EBC_vs_EC_pY <- add_rejections_SH(data_diff_EBC_vs_EC_pY, alpha = 0.05, lfc = log2(1.2))
GGPlotly_Volcano(dep_EBC_vs_EC_pY, contrast = "EBC_vs_EC",  add_names = TRUE, additional_title = "pY")
Return_DEP_Hits_Plots(data = pY_Set2_form, dep_EBC_vs_EC_pY, comparison = "EBC_vs_EC_diff")
## 'select()' returned 1:many mapping between keys and columns
## 'select()' returned 1:many mapping between keys and columns
## 'select()' returned 1:1 mapping between keys and columns
##                                                            pathway      pval
## 1: A tetrasaccharide linker sequence is required for GAG synthesis 0.2646240
## 2:                                       ABC transporter disorders 0.4969988
## 3:                          ABC-family proteins mediated transport 0.6323185
## 4:                       ADP signalling through P2Y purinoceptor 1 0.8245342
## 5:                                           ALK mutants bind TKIs 0.1710262
## 6:               APC/C-mediated degradation of cell cycle proteins 0.3325527
##         padj    log2err         ES        NES size        leadingEdge
## 1: 0.7624862 0.14122512  0.7187500  1.1637136    2          1464,6385
## 2: 0.8115798 0.05434344 -0.5069814 -0.9879145    5     5693,5687,5696
## 3: 0.8994749 0.04216194 -0.4390481 -0.8957048    6     5693,5687,5696
## 4: 0.9446513 0.04293111 -0.5026042 -0.7693801    2          6714,1432
## 5: 0.5935614 0.15016980 -0.9220779 -1.2273976    1               1213
## 6: 0.8115798 0.07253519 -0.5427770 -1.1073227    6 5693,983,5687,5696
## Warning in max(screen_pval05_pos[, logFcColStr]): no non-missing arguments to
## max; returning -Inf
## Warning in min(x): no non-missing arguments to min; returning Inf
## Warning in max(x): no non-missing arguments to max; returning -Inf
## Warning in min(x): no non-missing arguments to min; returning Inf
## Warning in max(x): no non-missing arguments to max; returning -Inf
## Warning in min(cs1s, na.rm = TRUE): no non-missing arguments to min; returning
## Inf
## Warning in max(cs1s, na.rm = TRUE): no non-missing arguments to max; returning
## -Inf
## Warning in min(cs2s, na.rm = TRUE): no non-missing arguments to min; returning
## Inf
## Warning in max(cs2s, na.rm = TRUE): no non-missing arguments to max; returning
## -Inf
## Warning in min(cs3s, na.rm = TRUE): no non-missing arguments to min; returning
## Inf
## Warning in max(cs3s, na.rm = TRUE): no non-missing arguments to max; returning
## -Inf

## Note: Row-scaling applied for this heatmap

#data_results <- get_df_long(dep)

Session Info

sessionInfo()
## R version 4.1.3 (2022-03-10)
## Platform: x86_64-apple-darwin17.0 (64-bit)
## Running under: macOS Big Sur/Monterey 10.16
## 
## Matrix products: default
## BLAS:   /Library/Frameworks/R.framework/Versions/4.1/Resources/lib/libRblas.0.dylib
## LAPACK: /Library/Frameworks/R.framework/Versions/4.1/Resources/lib/libRlapack.dylib
## 
## locale:
## [1] en_US.UTF-8/en_US.UTF-8/en_US.UTF-8/C/en_US.UTF-8/en_US.UTF-8
## 
## attached base packages:
## [1] stats4    stats     graphics  grDevices utils     datasets  methods  
## [8] base     
## 
## other attached packages:
##  [1] forcats_0.5.2               stringr_1.4.1              
##  [3] dplyr_1.0.10                purrr_0.3.5                
##  [5] readr_2.1.3                 tidyr_1.2.1                
##  [7] tibble_3.1.8                ggplot2_3.3.6              
##  [9] tidyverse_1.3.2             mdatools_0.13.0            
## [11] SummarizedExperiment_1.24.0 GenomicRanges_1.46.1       
## [13] GenomeInfoDb_1.30.1         MatrixGenerics_1.6.0       
## [15] matrixStats_0.62.0          DEP_1.16.0                 
## [17] org.Hs.eg.db_3.14.0         AnnotationDbi_1.56.2       
## [19] IRanges_2.28.0              S4Vectors_0.32.4           
## [21] Biobase_2.54.0              BiocGenerics_0.40.0        
## [23] fgsea_1.20.0               
## 
## loaded via a namespace (and not attached):
##   [1] utf8_1.2.2             shinydashboard_0.7.2   proto_1.0.0           
##   [4] gmm_1.7                tidyselect_1.2.0       RSQLite_2.2.18        
##   [7] htmlwidgets_1.5.4      grid_4.1.3             BiocParallel_1.28.3   
##  [10] norm_1.0-10.0          munsell_0.5.0          codetools_0.2-18      
##  [13] preprocessCore_1.56.0  chron_2.3-58           DT_0.26               
##  [16] withr_2.5.0            colorspace_2.0-3       highr_0.9             
##  [19] knitr_1.40             rstudioapi_0.14        mzID_1.32.0           
##  [22] labeling_0.4.2         GenomeInfoDbData_1.2.7 pheatmap_1.0.12       
##  [25] bit64_4.0.5            farver_2.1.1           vctrs_0.5.0           
##  [28] generics_0.1.3         xfun_0.34              R6_2.5.1              
##  [31] doParallel_1.0.17      clue_0.3-62            MsCoreUtils_1.6.2     
##  [34] bitops_1.0-7           cachem_1.0.6           DelayedArray_0.20.0   
##  [37] assertthat_0.2.1       promises_1.2.0.1       scales_1.2.1          
##  [40] googlesheets4_1.0.1    gtable_0.3.1           affy_1.72.0           
##  [43] sandwich_3.0-2         rlang_1.0.6            mzR_2.28.0            
##  [46] GlobalOptions_0.1.2    lazyeval_0.2.2         gargle_1.2.1          
##  [49] impute_1.68.0          broom_1.0.1            BiocManager_1.30.19   
##  [52] yaml_2.3.6             modelr_0.1.9           crosstalk_1.2.0       
##  [55] backports_1.4.1        httpuv_1.6.6           tools_4.1.3           
##  [58] affyio_1.64.0          ellipsis_0.3.2         gplots_3.1.3          
##  [61] jquerylib_0.1.4        RColorBrewer_1.1-3     STRINGdb_2.6.5        
##  [64] MSnbase_2.20.4         gsubfn_0.7             Rcpp_1.0.9            
##  [67] hash_2.2.6.2           plyr_1.8.7             zlibbioc_1.40.0       
##  [70] RCurl_1.98-1.9         sqldf_0.4-11           GetoptLong_1.0.5      
##  [73] zoo_1.8-11             haven_2.5.1            cluster_2.1.4         
##  [76] fs_1.5.2               magrittr_2.0.3         data.table_1.14.4     
##  [79] circlize_0.4.15        reprex_2.0.2           reactome.db_1.77.0    
##  [82] googledrive_2.0.0      pcaMethods_1.86.0      mvtnorm_1.1-3         
##  [85] ProtGenerics_1.26.0    hms_1.1.2              mime_0.12             
##  [88] evaluate_0.17          xtable_1.8-4           XML_3.99-0.12         
##  [91] readxl_1.4.1           gridExtra_2.3          shape_1.4.6           
##  [94] compiler_4.1.3         KernSmooth_2.23-20     ncdf4_1.19            
##  [97] crayon_1.5.2           htmltools_0.5.3        later_1.3.0           
## [100] tzdb_0.3.0             lubridate_1.8.0        DBI_1.1.3             
## [103] dbplyr_2.2.1           ComplexHeatmap_2.10.0  MASS_7.3-58.1         
## [106] tmvtnorm_1.5           Matrix_1.5-1           cli_3.4.1             
## [109] vsn_3.62.0             imputeLCMD_2.1         parallel_4.1.3        
## [112] igraph_1.3.5           pkgconfig_2.0.3        plotly_4.10.0         
## [115] MALDIquant_1.21        xml2_1.3.3             foreach_1.5.2         
## [118] bslib_0.4.0            XVector_0.34.0         rvest_1.0.3           
## [121] digest_0.6.30          Biostrings_2.62.0      rmarkdown_2.17        
## [124] cellranger_1.1.0       fastmatch_1.1-3        shiny_1.7.3           
## [127] gtools_3.9.3           rjson_0.2.21           lifecycle_1.0.3       
## [130] jsonlite_1.8.3         viridisLite_0.4.1      limma_3.50.3          
## [133] fansi_1.0.3            pillar_1.8.1           lattice_0.20-45       
## [136] KEGGREST_1.34.0        fastmap_1.1.0          httr_1.4.4            
## [139] plotrix_3.8-2          glue_1.6.2             fdrtool_1.2.17        
## [142] png_0.1-7              iterators_1.0.14       bit_4.0.4             
## [145] stringi_1.7.8          sass_0.4.2             blob_1.2.3            
## [148] caTools_1.18.2         memoise_2.0.1
knitr::knit_exit()